class_docstr: ---------------------------------------------------------------------------
  Testing ViT individual blocks
  ---------------------------------------------------------------------------
code: def _block_worker(
      gpu_id: int,
      with_fsdp: bool,
      with_amp: bool,
      sync_file: str,
      result_file: str,
  ):
      init_distributed_on_file(
          world_size=2, gpu_id=gpu_id, sync_file=sync_file
      )
      torch.manual_seed(0)
      torch.backends.cudnn.deterministic = True
      torch.cuda.reset_peak_memory_stats()
  
      # Create the inputs
      batch_size = 8
      embed_dim = 384
      batch = torch.randn(size=(batch_size, 3, 224, 224)).cuda()
  
      # Create the model
      num_blocks = 5
      patch_embed = PatchEmbed(embed_dim=embed_dim).cuda()
      blocks = [
          Block(dim=embed_dim, num_heads=6).cuda()
          for _ in range(num_blocks)
      ]
      norm = nn.LayerNorm(embed_dim).cuda()
  
      # Wrap the model with FSDP or DDP
      if with_fsdp:
          fsdp_config = {
              "flatten_parameters": True,
              "mixed_precision": with_amp,
              "fp32_reduce_scatter": False,  # Only makes sense to be True when mixed_precision is True.
              "compute_dtype": torch.float32,
              "bucket_cap_mb": 0,
              "clear_autocast_cache": True,
              "verbose": True,
              "reshard_after_forward": True,
          }
          blocks = [
              fsdp_wrapper(block, **fsdp_config) for block in blocks
          ]
          model = nn.Sequential(patch_embed, *blocks, norm)
          model = fsdp_wrapper(model, **fsdp_config)
      else:
          model = nn.Sequential(patch_embed, *blocks, norm)
          model = DistributedDataParallel(model, device_ids=[gpu_id])
  
      # Print the model
      if gpu_id == 0:
          print(model)
  
      # Create the optimizer
      param_groups = [
          {
              "params": model.parameters(),
              "lr": 1e-4,
              "weight_decay": 1e-3,
          }
      ]
      optimizer = optim.AdamW(param_groups)
  
      # Go through several training loops
      losses = []
      for step in range(5):
          # Setup the AMP context if necessary
          context = contextlib.suppress()
          if with_amp:
              context = torch.cuda.amp.autocast()
  
          # Forward pass
          with context:
              out = model(batch)
              out = out.mean()
  
          # Backward pass
          if torch_version() >= (1, 7, 0):
              model.zero_grad(set_to_none=True)
          else:
              model.zero_grad()
          out.backward()
          optimizer.step()
  
          # Report results and run schedulers
          torch.distributed.all_reduce(out)
          losses.append(out.item())
          optimizer.param_groups[0].update(
              {
                  "params": model.parameters(),
                  "lr": 1e-4 + step * 1e-4,
                  "weight_decay": 1e-3 + step * 1e-3,
              }
          )
  
          # Report memory usage
          if gpu_id == 0:
              print(torch.cuda.max_memory_allocated() // 1e6)
  
      # Dump the list of losses
      if gpu_id == 0:
          print(losses)
          with open(result_file, "wb") as f:
              pickle.dump(losses, f)
  
imports: import contextlib
  import pickle
  import unittest
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from vissl.config import AttrDict
  from vissl.models import build_model
  from vissl.models.heads import DINOHead
  from vissl.models.heads.dino_head import DINOHeadFSDP
  from vissl.models.trunks.vision_transformer import Block, PatchEmbed
  from vissl.utils.fsdp_utils import fsdp_wrapper, is_valid_fsdp_model
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.misc import torch_version
  from vissl.utils.test_utils import gpu_test, init_distributed_on_file, with_temp_files
label_desc: Use image node_id 3 for calling the TestVitFSDP obj's underlying member method code with example usage: obj._block_worker(gpu_id, with_fsdp, with_amp, sync_file, result_file) without return types